In this paper we develop an automatic classifier for a very large set of labels, the WordNet synsets. We employ Conditional Random Fields (CRFs) because of their flexibility to include a wide variety of nonindependent features. Training CRFs on a big number of labels proved a problem because of the large training cost. By taking into account the hypernym/hyponym relation between synsets in Word...

A test of directional entailment properties of classical quantifiers defined by the theory of generalised quantifiers (Barwise and Cooper, 1981) is described. Participants had to solve a task which consisted of four kinds of inference. In the first one, the premise was of the type Q-hyponym-verb-blank predicate, where Q is a classical quantifier, (e. g., some cats are [ ]), and the question was...

As the amount of electronic documents (corpora, dictionaries, newspapers, newswires, etc.) becomes more and more important and diversified, there is a need to extract information automatically from these texts. In order to extract terms and relations between terms, two methods can be used. The first method is the unsupervised approach, which requires a term extraction module and few predefined ...

This paper presents our relation extraction system for subtask C of SemEval-2017 Task 10: ScienceIE. Assuming that the keyphrases are already annotated in the input data, our work explores a wide range of linguistic features, applies various feature selection techniques, optimizes the hyper parameters and class weights and experiments with different problem formulations (single classification m...

Semantic hierarchy construction aims to build structures of concepts linked by hypernym–hyponym (“is-a”) relations. A major challenge for this task is the automatic discovery of such relations. This paper proposes a novel and effective method for the construction of semantic hierarchies based on word embeddings, which can be used to measure the semantic relationship between words. We identify w...

This paper describes and analyzes our SemEval 2014 Task 1 system. Its features are based on distributional and denotational similarities; word alignment; negation; and hypernym/hyponym, synonym, and antonym relations.

This paper proposes an extension of Sumida and Torisawa’s method of acquiring hyponymy relations from hierachical layouts in Wikipedia (Sumida and Torisawa, 2008). We extract hyponymy relation candidates (HRCs) from the hierachical layouts in Wikipedia by regarding all subordinate items of an item x in the hierachical layouts as x’s hyponym candidates, while Sumida and Torisawa (2008) extracted...

While paradigmatic lexical/sense relations are traditionally used to analyse the between lexical items, present article adopts a descriptive qualitative method, based on Cruse’s (2000) classification of relations, find identity and inclusion two items in different texts by using logical entailment relation. The aims at finding out whether or not can be substituted another without changing their...

We propose and evaluate a method for identifying co-hyponym lexical units in a terminological resource. The principles of term recognition and distributional semantics are combined to extract terms from a similar category of concept. Given a set of candidate terms, random projections are employed to represent them as low-dimensional vectors. These vectors are derived automatically from the freq...

The problem statement of this research is hyponym effective towards students’ vocabulary at the first semester English Department FBMB. aim to find out effect using in teaching This used quantitative with experimental design. was conducted on September November 2021. total students were 120 students. researcher chose two classes as sample research, namely control and groups. In group treated by...